Please refer to the following question:

Suppose you are the manager of a data analytics company. One of yourjunior analysts has applied a decision tree classification algorithm to a labeled data set D and calculated two things: 1) the accuracy of the decision tree on D using 10-fold cross validation, and 2) the accuracy of the decision tree on D using an 80%-train /20%-test split of the data She says to you: \"With 10-fold cross validation, we get a 12% improvement in accuracy compared to the 80/20 split. I think that for classifying new data, we should use 10-fold cross validation\". You realize that the analyst found something interesting, but she is also lacking a fundamental understanding of data mining. What is incorrect in the analyst's statement that \"I think that for classifying new data, we should use 10-fold cross validation"? Consider a Boolean classification problem with a yeso class label (and some features). Consider a classifier called VERY_S|MPLE that works as follows. Let C be the class label that occurs more frequently in the training dataset {if both yes and no are equally frequent, let C = "yes\" to break the tie). When given a new datapoint to classify, VERY_S|MPLE will always output C, regardless of the values of the features of the new datapoint. Suppose we have a dataset with exactly one half of the records with class "yes" and exactly one half with class "no". Compute the accuracy of VERY_SIMPLE on this dataset using leave-one-out cross validation